Particulate matter with an aerodynamic diameter of 2.5 μm or less (PM2.5) is the main air pollutant in Beijing. To have a comprehensive understanding of concentrations, compositions and sources of ...PM2.5 in Beijing, recent studies reporting ground-based observations and source apportionment results dated from 2000 to 2012 in this typical large city of China are reviewed. Statistical methods were also used to better enable data comparison. During the last decade, annual average concentrations of PM2.5 have decreased and seasonal mean concentrations declined through autumn and winter. Generally, winter is the most polluted season and summer is the least polluted one. Seasonal variance of PM2.5 levels decreased. For diurnal variance, PM2.5 generally increases at night and decreases during the day. On average, organic matters, sulfate, nitrate and ammonium are the major compositions of PM2.5 in Beijing. Fractions of organic matters increased from 2000 to 2004, and decreased afterwards. Fractions of sulfate, nitrate and ammonium decreased in winter and remained largely unchanged in summer. Concentrations of organic carbon and elemental carbon were always higher in winter than in summer and they barely changed during the last decade. Concentrations of sulfate, nitrate and ammonium exhibited significant increasing trend in summer but in reverse in winter. On average they were higher in winter than in summer before 2005, and took a reverse after 2005. Receptor model results show that vehicle, dust, industry, biomass burning, coal combustion and secondary products were major sources and they all increased except coal combustions and secondary products. The growth was decided both changing social and economic activities in Beijing, and most likely growing emissions in neighboring Hebei province. Explicit descriptions of the spatial variations of PM2.5 concentration, better methods to estimate secondary products and ensemble source apportionments models to reduce uncertainties would remain being open questions for future studies.
•Data from over sixty studies on PM2.5 in Beijing from 2000 to 2012 are reviewed.•Annual average PM2.5 concentrations decrease from 2000 to 2012.•Concentrations of OC and EC remain unchanged from 2000 to 2010.•Concentrations of SNA decrease in winter and increase in summer from 2000 to 2010.•Sources of PM2.5 correlate closely with social and economic development.
Estimating exposures to PM2.5 within urban areas requires surface PM2.5 concentrations at high temporal and spatial resolutions. We developed a mixed effects model to derive daily estimations of ...surface PM2.5 levels in Beijing, using the 3 km resolution satellite aerosol optical depth (AOD) calibrated daily by the newly available high-density surface measurements. The mixed effects model accounts for daily variations of AOD-PM2.5 relationships and shows good performance in model predictions (R 2 of 0.81–0.83) and cross-validations (R 2 of 0.75–0.79). Satellite derived population-weighted mean PM2.5 for Beijing was 51.2 μg/m3 over the study period (Mar 2013 to Apr 2014), 46% higher than China’s annual-mean PM2.5 standard of 35 μg/m3. We estimated that more than 19.2 million people (98% of Beijing’s population) are exposed to harmful level of long-term PM2.5 pollution. During 25% of the days with model data, the population-weighted mean PM2.5 exceeded China’s daily PM2.5 standard of 75 μg/m3. Predicted high-resolution daily PM2.5 maps are useful to identify pollution “hot spots” and estimate short- and long-term exposure. We further demonstrated that a good calibration of the satellite data requires a relatively large number of ground-level PM2.5 monitoring sites and more are still needed in Beijing.
Summary The Lancet Countdown: tracking progress on health and climate change is an international, multidisciplinary research collaboration between academic institutions and practitioners across the ...world. It follows on from the work of the 2015 Lancet Commission, which concluded that the response to climate change could be “the greatest global health opportunity of the 21st century”. The Lancet Countdown aims to track the health impacts of climate hazards; health resilience and adaptation; health co-benefits of climate change mitigation; economics and finance; and political and broader engagement. These focus areas form the five thematic working groups of the Lancet Countdown and represent different aspects of the complex association between health and climate change. These thematic groups will provide indicators for a global overview of health and climate change; national case studies highlighting countries leading the way or going against the trend; and engagement with a range of stakeholders. The Lancet Countdown ultimately aims to report annually on a series of indicators across these five working groups. This paper outlines the potential indicators and indicator domains to be tracked by the collaboration, with suggestions on the methodologies and datasets available to achieve this end. The proposed indicator domains require further refinement, and mark the beginning of an ongoing consultation process—from November, 2016 to early 2017—to develop these domains, identify key areas not currently covered, and change indicators where necessary. This collaboration will actively seek to engage with existing monitoring processes, such as the UN Sustainable Development Goals and WHO's climate and health country profiles. The indicators will also evolve over time through ongoing collaboration with experts and a range of stakeholders, and be dependent on the emergence of new evidence and knowledge. During the course of its work, the Lancet Countdown will adopt a collaborative and iterative process, which aims to complement existing initiatives, welcome engagement with new partners, and be open to developing new research projects on health and climate change.
Although the Advanced Topographic Laser Altimeter System (ATLAS) onboard the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) was primarily designed for glacier and sea-ice measurement, it can ...also be applied to monitor lake surface height (LSH). However, its performance in monitoring lakes/reservoirs has rarely been assessed. Here, we report an accuracy evaluation of the ICESat-2 laser altimetry data over 30 reservoirs in China using gauge data. To show its characteristics in large-scale lake monitoring, we also applied an advanced radar altimeter SARAL (Satellite for ARgos and ALtika) and the first laser altimeter ICESat (Ice, Cloud and land Elevation Satellite) to investigate all lakes and reservoirs (>10 km2) in China. We found that the ICESat-2 has a greatly improved altimetric capability, and the relative altimetric error was 0.06 m, while the relative altimetric error was 0.25 m for SARAL. Compared with SARAL and ICESat data, ICESat-2 data had the lowest measurement uncertainty (the standard deviation of along-track heights; 0.02 m vs. 0.17 m and 0.07 m), the greatest temporal frequency (3.43 vs. 1.35 and 1.48 times per year), and the second greatest lake coverage (636 vs. 814 and 311 lakes). The precise LSH profiles derived from the ICESat-2 data showed that most lakes (90% of 636 lakes) had a quasi-horizontal LSH profile (measurement uncertainty <0.05 m), and special methods are needed for mountainous lakes or shallow lakes to extract precise LSHs.
Land cover in Beijing experienced a dramatic change due to intensive human activities, such as urbanization and afforestation. However, the spatial patterns of the dynamics are still unknown. The ...archived Landsat images provide an unprecedented opportunity to detect land cover changes over the past three decades. In this study, we used the Normalized Difference Vegetation Index (NDVI) trajectory to detect major land cover dynamics in Beijing. Then, we classified the land cover types in 2015 with the Google Earth Engine (GEE) cloud calculation. By overlaying the latest land cover types and the spatial distribution of land cover dynamics, we determined the main types where a land cover change occurred. The overall change detection accuracy for three types (vegetation loss associated with negative change in NDVI, vegetation gain associated with positive change in NDVI, and no changes) is 86.13%. We found that the GEE is a fast and powerful tool for land cover mapping, and we obtained a classification map with an overall accuracy of 86.61%. Over the past 30years, 1402.28km2 of land was with vegetation loss and 1090.38km2 of land was revegetated in Beijing. The spatial pattern of vegetation loss and vegetation gain shows significant differences in different zones from the center of the city. We also found that 1162.71km2 of land was converted to urban and built-up, whereas 918.36km2 of land was revegetated to cropland, shrub land, forest, and grassland. Moreover, 202.67km2 and 156.75km2 of the land was transformed to forest and shrub land in the plain of Beijing that were traditionally used for cropland and housing.
•We used annual NDVI time-series to detect the land cover dynamics in Beijing.•We used cloud computation in the GEE to map the most recent land cover in 2015.•We obtained a classification map with an overall accuracy of 86.61%.•We found vegetation loss and vegetation gain patterns over the past three decades.•1402.28km2 of land was with vegetation loss and 1090.38km2 was revegetated.
Land cover is the physical material at the surface of the Earth. As the cause and result of global environmental change, land cover
change (LCC) influences the global energy balance and ...biogeochemical cycles.
Continuous and dynamic monitoring of global LC is urgently needed. Effective
monitoring and comprehensive analysis of LCC at the global scale are rare.
With the latest version of GLASS (Global Land Surface Satellite) CDRs
(climate data records) from 1982 to 2015, we built the first record of
34-year-long annual dynamics of global land cover (GLASS-GLC) at 5 km
resolution using the Google Earth Engine (GEE) platform. Compared to earlier
global land cover (LC) products, GLASS-GLC is characterized by high consistency, more
detail, and longer temporal coverage. The average overall accuracy for the
34 years each with seven classes, including cropland, forest, grassland,
shrubland, tundra, barren land, and snow/ice, is 82.81 % based on 2431
test sample units. We implemented a systematic uncertainty analysis and
carried out a comprehensive spatiotemporal pattern analysis. Significant
changes at various scales were found, including barren land loss and
cropland gain in the tropics, forest gain in the Northern Hemisphere, and
grassland loss in Asia. A global quantitative analysis of human factors
showed that the average human impact level in areas with significant LCC was
about 25.49 %. The anthropogenic influence has a strong correlation with
the noticeable vegetation gain, especially for forest. Based on GLASS-GLC,
we can conduct long-term LCC analysis, improve our understanding of global
environmental change, and mitigate its negative impact. GLASS-GLC will be
further applied in Earth system modeling to facilitate research on global
carbon and water cycling, vegetation dynamics, and climate change. The
GLASS-GLC data set presented in this article is available at
https://doi.org/10.1594/PANGAEA.913496 (Liu et al., 2020).
Urban boundaries, an essential property of cities, are widely used in many urban studies. However, extracting urban boundaries from satellite images is still a great challenge, especially at a global ...scale and a fine resolution. In this study, we developed an automatic delineation framework to generate a multi-temporal dataset of global urban boundaries (GUB) using 30 m global artificial impervious area (GAIA) data. First, we delineated an initial urban boundary by filling inner non-urban areas of each city. A kernel density estimation approach and cellular-automata based urban growth modeling were jointly used in this step. Second, we improved the initial urban boundaries around urban fringe areas, using a morphological approach by dilating and eroding the derived urban extent. We implemented this delineation on the Google Earth Engine platform and generated a 30 m resolution global urban boundary dataset in seven representative years (i.e. 1990, 1995, 2000, 2005, 2010, 2015, and 2018). Our extracted urban boundaries show a good agreement with results derived from nighttime light data and human interpretation, and they can well delineate the urban extent of cities when compared with high-resolution Google Earth images. The total area of 65 582 GUBs, each of which exceeds 1 km2, is 809 664 km2 in 2018. The impervious surface areas account for approximately 60% of the total. From 1990 to 2018, the proportion of impervious areas in delineated boundaries increased from 53% to 60%, suggesting a compact urban growth over the past decades. We found that the United States has the highest per capita urban area (i.e. more than 900 m2) among the top 10 most urbanized nations in 2018. This dataset provides a physical boundary of urban areas that can be used to study the impact of urbanization on food security, biodiversity, climate change, and urban health. The GUB dataset can be accessed from http://data.ess.tsinghua.edu.cn.
Outdoor air pollution is a major killer worldwide and the fourth largest contributor to the burden of disease in China. China is the most populous country in the world and also has the largest number ...of air pollution deaths per year, yet the spatial resolution of existing national air pollution estimates for China is generally relatively low. We address this knowledge gap by developing and evaluating national empirical models for China incorporating land-use regression (LUR), satellite measurements, and universal kriging (UK). Land use, traffic and meteorological variables were included for model building. We tested the resulting models in several ways, including (1) comparing models developed using forward variable selection vs. partial least squares (PLS) variable reduction, (2) comparing models developed with and without satellite measurements, and with and without UK, and (3) 10-fold cross-validation (CV), Leave-One-Province-Out CV (LOPO-CV), and Leave-One-City-Out CV (LOCO-CV). Satellite data and kriging are complementary in making predictions more accurate: kriging improved the models in well-sampled areas; satellite data substantially improved performance at locations far away from monitors. Variable-selection models performed similarly to PLS models in 10-fold CV, but better in LOPO-CV. Our best models employed forward variable selection and UK, with 10-fold CV R2 of 0.89 (for both 2014 and 2015) for PM2.5 and of 0.73 (year-2014) and 0.78 (year-2015) for NO2. Population-weighted concentrations during 2014–2015 decreased for PM2.5 (58.7 μg/m3 to 52.3 μg/m3) and NO2 (29.6 μg/m3 to 26.8 μg/m3). We produced the first high resolution national LUR models for annual-average concentrations in China. Models were applied on 1 km grid to support future research. In 2015, >80% of the Chinese population lived in areas that exceeded the Chinese national PM2.5 standard, 35 μg/m3. Results here will be publicly available and may be useful for epidemiology, risk assessment, and environmental justice research.
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•First high spatial resolution national LUR models for both NO2 and PM2.5 in China•Satellite data and kriging are complementary in making predictions more accurate.•Variable selection models perform similar or better than PLS models.•1 km2 resolution prediction maps will be publicly available for future research.
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•The geometrical and electronic structures of adsorption of HNO on Al12N12 nano-cage have been examined utilizing DFT.•The Al12N12 nano-cage is Ф-type sensor and electronic sensor for ...HNO.•Atoms in molecules (AIM) and UV-Vis spectrum analysis were calculated.
The geometrical and electronic structures of adsorption of HNO on Al12N12 nanocage have been examined utilizing DFT computations by means of a (B3LYP-D) function with 6-31G* basis set. It was observed that HNO prefers to be adsorbed on the cage oxygen atom with −0.70 eV adsorption energy. This process of adsorption remarkably decreases the HOMO-LUMO gap (Eg) of the cage from 3.91 to 1.41 eV. The time-dependent density functional theory (TDDFT) indicates a high intensity peak in 390.29 nm in the steadiest complexes of HNO with Al12N12. The alteration in electronic attributes of the Al12N12 nanocage on adsorption of HNO is enough to consider it a potential sensor for the detection of HNO. The Al12N12 nanocage is Ф-type sensor and electronic sensor for HNO.
Metadata information and catalogue services are major ways of making satellite images findable and accessible. Spatio-temporal indexing is the key to ensuring efficient searches. Because spatial ...information and temporal information are usually independently maintained and indexed, the image retrieval process has to include two search steps: a spatial query and a temporal query. As most Earth Observation satellites are specially designed to have repeating sun-synchronous orbits (RSSO), this type of satellite data has a close correlation between its spatial coverage and temporal coverage information. In this paper, an integrated spatio-temporal indexing mechanism is proposed for RSSO satellites. The spatio-temporal Look-Up Table (st-LUT) that serves as the index reflects the coupled correlation between the spatial and temporal coverage information within one orbit revisiting cycle. Image retrieval algorithms are designed based on the st-LUT. In this study, 1,765,797 Landsat 8 scenes collected from 28 June 2013 to 31 December 2019 data are used to establish and validate the proposed indexing mechanism and search algorithms. Because this new method only need to focus on the changes of the spatial and temporal coverage over the time in one orbit revisiting cycle, the spatial search space is limited to the fixed number of grids. Therefore, the search algorithm is at a constant level. Its performance is not related to the volume of the images that need to search.
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